Ballistocardiography (BCG) is considered a good alternative to HRV analysis with its non-contact and unobtrusive acquisition characteristics. However, consensus about its validity has not yet been established. In this study, 50 healthy subjects (26.2 ± 5.5 years old, 22 females, 28 males) were invited. Comprehensive statistical analysis, including Coefficients of Variation (CV), Lin’s Concordance Correlation Coefficient (LCCC), and Bland-Altman analysis (BA ratio), were utilized to analyze the consistency of BCG and ECG signals in HRV analysis. If the methods gave different answers, the worst case was taken as the result. Measures of consistency such as Mean, SDNN, LF gave good agreement (the absolute value of CV difference < 2%, LCCC > 0.99, BA ratio < 0.1) between J-J (BCG) and R-R intervals (ECG). pNN50 showed moderate agreement (the absolute value of CV difference < 5%, LCCC > 0.95, BA ratio < 0.2), while RMSSD, HF, LF/HF indicated poor agreement (the absolute value of CV difference ≥ 5% or LCCC ≤ 0.95 or BA ratio ≥ 0.2). Additionally, the R-R intervals were compared with P-P intervals extracted from the pulse wave (PW). Except for pNN50, which exhibited poor agreement in this comparison, the performances of the HRV indices estimated from the PW and the BCG signals were similar.

Atrial fibrillation (AF) is the most common arrhythmia and can seriously threaten patient health. Research on AF detection carries important clinical significance. This manuscript proposes an AF detection method based on ballistocardiogram (BCG) signals collected by a noncontact sensor. We first constructed a BCG signal dataset consisting of 28,214 ten-second nonoverlapping segments collected from 45 inpatients during overnight sleep, including 9438 for AF, 9570 for sinus rhythm (SR), and 9206 for motion artifacts (MA). Then, we designed a residual convolutional neural network (CNN) for AF detection. The network has four modules, namely a downsampling convolutional module, a local feature learning module, a global feature learning module, and a classification module, and it extracts local and global features from BCG signals for AF detection. The model achieved precision, sensitivity, specificity, F1 score, and accuracy of 96.8%, 93.7%, 98.4%, 95.2%, and 96.8%, respectively. The results indicate that the AF detection method proposed in this manuscript could serve as a basis for long-term screening of AF at home based on BCG signal acquisition.

Stiff person syndrome (SPS) is a rare disease characterized by axial and lower-extremity muscle rigidity, muscle spasm, and pain. Progressive encephalomyelitis with rigidity and myoclonus (PERM) is a variant of SPS. This case is particularly notable for its uncommon initial symptom: orthostatic hypotension, coupled with the presence of multiple antibodies. Such a presentation is a rarity in the context of PERM, thus providing a fresh and unique angle for both diagnosis and treatment. This case presents a 71-year-old man who was ultimately diagnosed with progressive encephalomyelitis with rigidity and myoclonus (PERM). His initial symptom was orthostatic hypotension, and we detected multiple antibodies such as GlyR antibody, GAD antibody, GM1-IgG and GQ1b-IgG in his serum. The patient showed partial response to glucocorticoid and immunoglobulin therapies, but as the disease recurred and progressed, plasma exchange, rituximab, and cyclophosphamide immunosuppressive therapy was administered, the prognosis remained poor. During follow-up after treatment, the patient developed pulmonary embolism and cardiac arrest, and died. PERM exhibits diverse manifestation and pathogenic mechanisms. Immune heterogeneity affects clinical symptoms and prognosis. Cases of PERM combined with orthostatic hypotension and various antibodies have rarely been reported, the incidence and the specific mechanism is unknown, underscoring the need for further research. This case report underscores the importance of recognizing the diverse clinical presentations of PERM and the challenges in its diagnosis and management. It highlights autonomic dysfunction may be as the initial symptom of PERM. Moreover, it emphasizes the limitations of current treatment modalities and the necessity for further research to elucidate the underlying mechanisms and optimize therapeutic approaches for this debilitating autoimmune condition.

Introduction: Obstructive sleep apnea syndrome (OSAS) degrades sleep quality and is associated with serious health conditions. Instead of the gold-standard polysomnography requiring complex equipment and expertise, a non-obtrusive device such as ballistocardiography (BCG) is more suitable for home-based continuous monitoring of OSAS, which has shown promising results in previous studies. However, often due to the limited storage and computing resource, also preferred by venders, the high computational cost in many existing BCG-based methods would practically limit the deployment for home monitoring.Methods: In this preliminary study, we propose an approach for OSAS monitoring using BCG signals. Applying fast change-point detection to first isolate apnea-suspected episodes would allow for processing only those suspected episodes for further feature extraction and OSAS severity classification. This can reduce both the data to be stored or transmitted and the computational load. Furthermore, our approach directly extracts features from BCG signals without employing a complex algorithm to derive respiratory and heart rate signals as often done in literature, further simplifying the algorithm pipeline. Apnea-hypopnea index (AHI) is then computed based on the detected apnea events (using a random forest classifier) from the identified apnea-suspected episodes. To deal with the expected underestimated AHI due to missing true apnea events during change-point detection, we apply boundary adjustment on AHI when classifying severity.Results: Cross-validated on 32 subjects, the proposed approach achieved an accuracy of 71.9% for four-class severity classification and 87.5% for binary classification (AHI less than 15 or not).Conclusion: These findings highlight the potential of our proposed BCG-based approach as an effective and accessible alternative for continuous OSAS monitoring.